Blood pressure prediction from speech recordings. (April 2020)

• Record Type: Journal Article
• Authors: Ankışhan, Haydar
• Is Part Of: Biomedical signal processing and control. Volume 58(2020)
• Publication Date: 2020-04
• Subjects: Feature extraction -- Blood pressure -- Hypertension -- Human voice and blood pressure interaction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101842
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The study shows that speech recordings and blood pressure values are closely related. It is possible to monitor the blood pressure of the individuals through their speech recordings. The feature vector indicates that high blood pressure symptoms can be tracked by the vowels of the individuals. Abstract: The aim of this study is to extract new features to show the relationship between speech recordings and blood pressure (BP). For this purpose, a database consisting of / a / vowels with different BP values under the same room and environment conditions is presented to the literature. Convolutional Neural Networks- Regression (CNN-R), Support Vector Machines- Regression (SVMs-R) and Multi Linear Regression (MLR) are used in this study to predict BP with extracted features. From the experiments, the highest accuracy rates of BP prediction from / a / vowel have been obtained based on Systolic BP values with CNN-R. In the study, 89.43 % for MLR, 92.15 % for SVM-R and 93.65 % for CNN-R are obtained when ReliefF has been used. When the root mean square errors (RMSE) are considered, the lowest error value is obtained with CNN-R as RMSE = 0.2355. In conclusion, it can be observed that the proposed feature vector (FVx) shows a relationship between BP and the human voices, and in this direction, it can be used as an FVx in a system that will be developed in order to follow the tension of individuals.